{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4  使用matplotlib绘制图表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "data = np.array([1, 2, 3, 4, 5])        # 准备数据\n",
    "fig = plt.figure()                      #  创建代表画布的Figure 类的对象fig\n",
    "ax = fig.add_subplot(111)               # 在画布fig上添加坐标系风格的绘图区域ax\n",
    "ax.plot(data)                           #  绘制图表\n",
    "plt.show()                              #  展示图表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt     # 导入 pyplot 模块\n",
    "data = np.array([1, 2, 3, 4, 5])     # 准备数据\n",
    "plt.plot(data)                       # 在当前画布的绘图区域中绘制图表\n",
    "plt.show()                           # 展示图表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
